MRI-based risk stratification for clinically significant prostate cancer detection at biopsy: The value of zonal-specific PSA density and PSHS
- 1Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland.
- 2Clinical Trials Center, University Hospital Zurich, Switzerland.
- 3Department of Biostatistics at Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Switzerland.
- 4Department of Pathology and Molecular Pathology, University Hospital Zurich, Switzerland; Faculty of Medicine, University of Zurich, Switzerland.
- 5Department of Urology, University Hospital Zurich, Switzerland.
- 0Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland.
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View abstract on PubMed
Summary
This summary is machine-generated.Combining prostate-specific antigen density (PSAD) variants with the Prostate Signal Intensity Homogeneity Score (PSHS) improves clinically significant prostate cancer (csPCa) detection. Specifically, transition zone PSAD (PSAD-TZ) and PSHS enhance risk stratification for biopsy decisions.
Area Of Science
- Radiology
- Urology
- Oncology
Background
- Multiparametric MRI (mpMRI) is crucial for prostate cancer detection.
- Accurate risk stratification is needed to optimize prostate biopsy selection.
- Zonal-specific PSA density (PSAD) variants offer potential for improved diagnostic accuracy.
Purpose Of The Study
- To evaluate zonal-specific PSAD variants (whole-gland, peripheral zone, transition zone) combined with Prostate Signal Intensity Homogeneity Score (PSHS) for detecting clinically significant prostate cancer (csPCa).
- To enhance risk stratification and patient selection for prostate biopsy.
- To investigate the combined diagnostic performance of PI-RADS, PSAD-TZ, and PSHS.
Main Methods
- Retrospective single-center study of 297 patients with suspected prostate cancer undergoing mpMRI and biopsy.
- Calculation of whole-gland (PSAD-T), peripheral zone (PSAD-PZ), and transition zone (PSAD-TZ) PSA densities from MRI-derived volumes.
- Assessment of diagnostic performance using ROC analysis and conditional inference trees for PI-RADS, PSAD-TZ, and PSHS.
Main Results
- csPCa was diagnosed in 126 (42.4%) patients.
- PSAD-TZ showed superior csPCa prediction performance (AUC 0.78) compared to PSAD-T (AUC 0.75) and PSAD-PZ (AUC 0.63).
- Patients with PI-RADS ≤ 3 and elevated PSAD-TZ with low PSHS scores (≤3) had an elevated risk of missed csPCa.
Conclusions
- Integrating PI-RADS, PSAD-TZ, and PSHS improves risk stratification for csPCa detection at biopsy.
- This combined approach enables more precise identification of high-risk patients requiring further evaluation.
- It may reduce false-negative MRI results and refine biopsy indication decisions.
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